quartz/content/notes/05-feature-description-and-matching.md

title	tags
05-feature-description-and-matching	lecture cosc342

last lecure: taking photographs and sticthcing them together. uses feature detection. identity interest points in image sequences. into homography matrix, map from one image into another

Blob Features

• corner point dont keep scale
• an alternative to corner features
  • have scale as well as location
• can be created using the "difference of gaussian method"
  • Two Gaussians of different width
  • Subtract wide from narrow
  • Bright blobs – high positive response
  • Dark blobs – high negative response
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FEATURE DESCRIPTION

• Features are matched on the basis of some descriptor
• This is usually a list of numbers, represented as a vector
• Often a high dimensional vector
• SIFT descriptors, for example, are 128-dimensional
• Matching descriptors should be close to each other
• This distance should be low even if the image changes
• Translation and rotation in the image plane
• Changes in viewing angle and distance (and therefore scale)
• Changes in illumination, brightness, and contrast

[!INFO] we will use sift descriptors alot.

A SIMPLE FEATURE DESCRIPTOR

• Could use pixels near the feature
• This is easy to do
• Works well in some cases
• Example is greyscale, but generalises easily to colour images
• Take an window ( odd)
• -dimensional feature vector
• Compare with Euclidean distance
• Often easier to use squared distance

Feature invariance

[!INFO] does not handle rotation, changing scale or brightness

SIFT: SCALE-INVARIANT FEATURE TRANSFORM

• Translation invariance is easy
• Scale invariance comes from using Blob features
• Descriptor is computed from a window around the feature
• The size of the blob determines the size of the window
• Brightness invariance comes from using image gradients
• Relative brightness of pixels is fairly constant
• Rotation invariance by estimating feature orientation
• Window is oriented to the dominant image gradient

[!INFO] not just direct comparision, compared on multiple, permutations

[!QUESTION] how is this different from doing different perumtations of grayscale vlues

[!INFO]

SIFT FEATURE DETECTION/DESCRIPTION

• Detect blob features and determine their scale
• Compute a Histogram of Gradients around the blob
• Peak(s) in the Histogram determine the orientation
• A square region is used to compute the descriptor
• Square’s size from the blob size; orientation from HoG peaks